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Open AccessArticle

Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

1
Department of Forest Mensuration and Forest Management, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str. 15, 03041 Kyiv, Ukraine
2
Bangor College China, Joint Unit of Bangor University, Bangor, UK and Central South University of Forestry and Technology, 498 Shaoshan Rd., Changsha 410004, Hunan, China
3
College of Life Sciences and Technology, National Engineering Laboratory of Applied Technology for Forestry and Ecology in South China, Central South University of Forestry and Technology, 498 Shaoshan Rd., Changsha 410004, Hunan, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 187; https://doi.org/10.3390/rs12010187
Received: 3 November 2019 / Revised: 22 December 2019 / Accepted: 3 January 2020 / Published: 5 January 2020
(This article belongs to the Special Issue Remote Sensing to Assess Canopy Structure and Function)
Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%. View Full-Text
Keywords: flatland Ukraine; forest cover map; forest mask; fragmented landscapes; global forest change; Google Earth Engine; Landsat imagery; random forest algorithm flatland Ukraine; forest cover map; forest mask; fragmented landscapes; global forest change; Google Earth Engine; Landsat imagery; random forest algorithm
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MDPI and ACS Style

Myroniuk, V.; Kutia, M.; J. Sarkissian, A.; Bilous, A.; Liu, S. Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification. Remote Sens. 2020, 12, 187.

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